The present invention generally relates to wireless communication systems, and particularly relates to processing received signals by such systems.
Signal quality estimation is a key feature of advanced wireless communication technologies such as Wideband CDMA (WCDMA), CDMA2000 1xRTT, CDMA2000 EV-DO, CDMA2000 EV-DV, etc. For high-speed data services in both the uplink and downlink directions, the receiver must determine and report a measure of signal quality such as signal to interference plus noise ratio (SINR) to the transmitter. The transmitter uses this information to determine what data rate (e.g., number of codes, modulation, coding, etc.) to send during the next transmission time interval (TTI).
Estimating the quality of a received WCDMA signal conventionally involves despreading the received signal, generating combining weights, and determining signal quality based on the combining weights. The standard model for despread WCDMA data is given by:xi=hsi+ui.  (1)where xi is the despread data vector for the ith symbol period, h is a vector of net channel coefficients (which accounts for the transmit/receive filters and radio channel), ui is a vector of interference plus noise (denoted impairment), and si is the ith symbol. Given a vector of combining weights w, symbol estimates for received data symbols may be obtained from:{circumflex over (z)}i=wHxi.  (2)SINR for the ith symbol is then given by:
                    SINR        =                                            E              s                        ⁢                                                                                                w                    H                                    ⁢                  h                                                            2                                                          w              H                        ⁢                          R              u                        ⁢            w                                              (        3        )            where Es=|si|2 and Ru is the impairment covariance matrix given by Ru=E{uuH}.
To achieve peak throughput, the receiver must correctly demodulate the transmitted data and accurately report SINR. This is particularly true for the Multiple-Input Multiple-Output (MIMO) and higher order modulation (HOM) schemes included in Release 7 of the WCDMA standard. MIMO and HOM can achieve higher throughput compared to previous methods, but require considerably higher signal quality to achieve peak rates.
Failure to accurately report SINR results in either overly aggressive or overly pessimistic coding/modulation at the transmitter. Either way, reduced overall throughput results. Past experience shows that signal quality estimation using conventional SINR estimators proves very difficult when signal quality is high. Conventional SINR estimators tend to yield biased or highly variant SINR estimates under these conditions. Signal demodulation likewise suffers when signal quality is high due to conventional means for estimating the impairment covariance matrix. Conventional impairment covariance estimation techniques are either highly complex or inaccurate when signal quality is high, thus adversely impacting high data rate users.
For example, in one conventional approach, a pilot channel is despread over a slot having ten pilot symbols, the modulation removed, and the sample impairment covariance matrix is computed from:
                                          y                          i              ,              pilot                                =                                    x                              i                ,                pilot                                      ⁢                          s              *                                      ⁢                                  ⁢                              y            _                    =                                    1              10                        ⁢                                          ∑                                  i                  =                  1                                10                            ⁢                              y                                  i                  ,                  pilot                                                                    ⁢                                  ⁢                                            R              ^                        u                    =                                    1              9                        ⁢                                          ∑                                  i                  =                  1                                10                            ⁢                                                (                                                            y                                              i                        ,                        pilot                                                              -                                                                  y                        _                                            pilot                                                        )                                ⁢                                                      (                                                                  y                                                  i                          ,                          pilot                                                                    -                                                                        y                          _                                                pilot                                                              )                                    H                                                                                        (        4        )            where xi,pilot is the despread pilot data and yi,pilot is the demodulated pilot data. Typically, the slot-based impairment covariance matrix estimate {circumflex over (R)}u of equation (4) is smoothed (i.e., averaged) over multiple slots to reduce estimation noise and provide reasonable performance. However, this approach requires considerable averaging to achieve good performance. While averaging does not adversely impact demodulation performance at low speed, demodulation at moderate to high speeds becomes impractical using this impairment covariance estimation approach. SINR estimation using this approach is also unreliable at moderate to high speeds.
In another conventional approach, the impairment covariance matrix is calculated using a model. The model is given by:
                              R          u                =                                            ∑                              j                =                1                            J                        ⁢                                                            E                  c                                ⁡                                  (                  j                  )                                            ⁢                                                R                  I                                ⁡                                  (                                      g                    j                                    )                                                              +                                    N              0                        ⁢                          R              n                                                          (        5        )            where Ec(j) represents the total energy per chip of base station j, RI(gj) is an interference term that depends on the radio (or medium) channel between the jth base station and the receiver gj, N0 represents the power of the white noise passing through the receive filter, and Rn is a thermal noise covariance term arising from the autocorrelation properties of receiver filtering. The receiver typically does not have knowledge of gj, Ec(j) or N0, so these parameters must be estimated.
This technique works well for low to moderate SINR conditions because gj, Ec(j) and N0 can be reliably estimated. However, when SINR becomes high, the ability to estimate N0 degrades substantially. As a result, the SINR estimates saturate. SINR saturation prevents a receiver from achieving the peak rates offered by MIMO and HOM.
Some conventional approaches directly estimate SINR by computing symbol estimates for the pilot channel over a slot as:{circumflex over (z)}i,pilot=wHxi,pilot  (6)The sample mean and sample variance of the pilot symbol estimates can then be calculated from:
                                          z            _                    =                                    1              10                        ⁢                                          ∑                                  i                  =                  1                                10                            ⁢                                                z                  ^                                                  i                  ,                  pilot                                                                    ⁢                                  ⁢                              σ            z            2                    =                                    1              9                        ⁢                                          ∑                                  i                  =                  1                                10                            ⁢                                                (                                                                                    z                        ^                                                                    i                        ,                        pilot                                                              -                                          z                      _                                                        )                                ⁢                                                      (                                                                                            z                          ^                                                                          i                          ,                          pilot                                                                    -                                              z                        _                                                              )                                    *                                                                                        (        7        )            SINR can be directly estimated via:
                    SINR        =                                            z              2                        -                                          σ                z                2                            /              10                                            σ            z            2                                              (        8        )            
Typically, direct estimation of SINR must be smoothed over multiple slots to reduce the variance of the estimator. The averaging needed for reducing the estimation variance causes a considerable difference between the actual SINR and the reported SINR. This difference may result in overly aggressive or overly pessimistic coding/modulation as previously explained.